Podcast
Questions and Answers
Which historical concept is related to Deep Learning?
Which historical concept is related to Deep Learning?
- Autoencoder
- McCulloch Pitts Neuron (correct)
- Regularization
- AdaGrad
What method is used for weight initialization in Deep Learning?
What method is used for weight initialization in Deep Learning?
- AdaGrad
- Batch Normalization (correct)
- Momentum Based GD
- Sigmoid Neurons
Which technique is used for dataset augmentation in Deep Learning?
Which technique is used for dataset augmentation in Deep Learning?
- PCA
- Adam
- Sigmoid Neurons
- Autoencoder (correct)
Which method is used to address overfitting in auto-encoders?
Which method is used to address overfitting in auto-encoders?
What type of neural network is commonly used in Deep Learning for unsupervised learning tasks?
What type of neural network is commonly used in Deep Learning for unsupervised learning tasks?
Flashcards
McCulloch-Pitts Neuron
McCulloch-Pitts Neuron
A foundational model for a single artificial neuron, crucial in early neural network development and serving as a precursor to Deep Learning.
Deep Learning Weight Initialization
Deep Learning Weight Initialization
Batch Normalization is a method used to initialize weights in Deep Learning models.
Dataset Augmentation Technique
Dataset Augmentation Technique
Autoencoders are used as a way to increase the size of a dataset.
Overfitting Solution in Autoencoders
Overfitting Solution in Autoencoders
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Unsupervised Learning Neural Network
Unsupervised Learning Neural Network
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Study Notes
Historical Concepts and Techniques in Deep Learning
- Connectionism, a historical concept, is related to Deep Learning, which is a subset of Machine Learning that involves neural networks with multiple layers.
Weight Initialization in Deep Learning
- Xavier initialization, a method, is used for weight initialization in Deep Learning to avoid the vanishing or exploding gradient problem.
Dataset Augmentation in Deep Learning
- Data transformation, a technique, is used for dataset augmentation in Deep Learning to artificially increase the diversity of the training dataset.
Overfitting in Auto-encoders
- Regularization, a method, is used to address overfitting in auto-encoders, which are neural networks that learn to copy their inputs.
Unsupervised Learning Tasks in Deep Learning
- Auto-encoder, a type of neural network, is commonly used in Deep Learning for unsupervised learning tasks, which involve training models on unlabeled data.
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